Update model card - stock

README.md

@@ -0,0 +1,144 @@
+---
+library_name: pytorch
+license: mit
+tags:
+- finance
+- trading
+- time-series
+- transformer
+- mamba
+- state-space-models
+- financial-ai
+- stock-prediction
+- forex-prediction
+---
+
+# MeridianAlgo Financial Prediction Models
+
+## Model Overview
+
+This repository contains financial prediction models built on the latest state-of-the-art 2026 architecture. These models leverage machine learning techniques, including Mamba State Space Models, Mixture of Experts, and Flash Attention 2, for accurate market forecasting.
+
+### Architecture Highlights
+
+- Latest 2026 Architecture: Implementing the latest advances in deep learning for time series.
+- 388M Parameters: Large-scale model for comprehensive pattern recognition.
+- Unified Design: A single high-capacity model architectures for all assets within its class.
+- Specialized Logic: Distinct optimization paths for Stocks and Forex markets.
+
+## Technical Specifications
+
+### Core Technologies
+
+| Component | Description |
+|-----------|-------------|
+| Mamba SSM | State Space Models for efficient long-range sequence modeling with linear complexity. |
+| RoPE | Rotary Position Embeddings for enhanced temporal relationship encoding. |
+| GQA | Grouped Query Attention for optimized computational throughput. |
+| MoE | Mixture of Experts with top-k routing for specialized regime recognition. |
+| SwiGLU | Swish-Gated Linear Unit activation for improved transformer performance. |
+| RMSNorm | Root Mean Square Normalization for enhanced gradient stability. |
+| Flash Attention 2 | High-performance, memory-efficient attention implementation. |
+
+### Model Specifications
+
+```
+Architecture: Revolutionary 2026
+Parameters: 388,000,000
+Input Features: 44 technical indicators
+Sequence Length: 30 time steps
+Hidden Dimensions: 768
+Transformer Layers: 6
+Attention Heads: 12 (Query), 4 (Key/Value)
+Experts: 12 specialized models
+Prediction Heads: 8 ensemble heads
+```
+## Available Models
+
+### 1. Meridian.AI Stocks Model
+- Repo ID: meridianal/ARA.AI
+- File: models/Meridian.AI_Stocks.pt
+- Purpose: Comprehensive stock market forecasting.
+- Coverage: Broad equity market compatibility.
+- Accuracy: Optimized for directional consistency.
+
+### 2. Meridian.AI Forex Model
+- Repo ID: meridianal/ARA.AI
+- File: models/Meridian.AI_Forex.pt
+- Purpose: High-precision currency pair forecasting.
+- Coverage: Major, Minor, and Exotic pairs.
+- Accuracy: Optimized for pip-based movement prediction.
+
+## Performance Metrics
+
+| Attribute | Stocks Model | Forex Model |
+|-----------|--------------|-------------|
+| Parameters | 388 Million | 388 Million |
+| Inference Latency | <100ms | <100ms |
+| Model Size | ~1.5 GB | ~1.5 GB |
+| Accuracy | Optimized | Optimized |
+
+## Usage and Implementation
+
+### Installation
+
+```bash
+pip install torch transformers huggingface_hub
+```
+
+### Loading and Inference
+
+```python
+from huggingface_hub import hf_hub_download
+from meridianalgo.unified_ml import UnifiedStockML
+
+# Download the unified stocks model
+model_path = hf_hub_download(
+    repo_id="meridianal/ARA.AI",
+    filename="models/Meridian.AI_Stocks.pt"
+)
+
+# Initialize the system
+ml = UnifiedStockML(model_path=model_path)
+
+# Execute prediction
+prediction = ml.predict_ultimate('AAPL', days=5)
+
+print(f"Current Price: {prediction['current_price']}")
+```
+
+## Training Methodology
+
+### Configuration
+- Optimizer: AdamW with Weight Decay
+- Scheduler: Cosine Annealing with Warm Restarts
+- Loss Function: Balanced Directional Loss
+- Batch Size: 64
+- Learning Rate: 0.0005
+
+### Infrastructure
+Models are trained using a distributed pipeline with Accelerate, tracking all metrics via Comet ML for rigorous validation.
+
+## Limitations and Ethical Use
+
+1. Market Volatility: Performance may degrade during black swan events or extreme volatility.
+2. Horizon Decay: Predictive accuracy naturally decreases as the forecast horizon extends.
+3. Historical Bias: Models reflect patterns in historical data which may not repeat.
+4. Professional Use Only: Intended for research; users bear all financial risk.
+
+## Citation
+
+If utilizing these models in professional research or applications, please cite:
+
+```bibtex
+@software{meridianalgo_2026,
+  title = {MeridianAlgo: Revolutionary Financial Prediction Platform},
+  author = {MeridianAlgo},
+  year = {2026},
+  version = {4.0.0}
+}
+```
+
+## Disclaimer
+
+IMPORTANT: These models are for research and educational purposes only. They do not constitute financial advice. All trading involves risk of capital loss. The developers and contributors are not registered financial advisors.